Vetted Apache Hadoop Professionals

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Vineeth Reddy Vallapureddy

Screened

Mid-level Full-Stack Software Engineer specializing in backend microservices and enterprise AI tools

Redwood City, California5y exp

“Backend/platform engineer with experience across C3.ai (supply chain demand planning) and Amdocs (telecom), working on large-scale data systems and microservices. Has driven first-time adoption experiments of Snowflake + Spark to handle billion-record workloads, built Jenkins-to-Kubernetes delivery pipelines with Nexus artifact management, and implemented Kafka streaming between microservices with HA and retry/error-handling patterns.”

Dhyey Desai

Screened

Intern AI/ML Engineer specializing in RAG, multimodal AI, and LLM systems

Los Angeles, California0y exp

“Built and shipped 'PetPulse,' a production AI pet-health note system that records voice notes, transcribes them, converts transcripts into structured symptom/event data, and supports grounded Q&A over a user’s notes and vet PDFs. Demonstrates full-stack LLM product execution (FastAPI + GPT-4 + Firebase), with concrete reliability/performance work (async endpoints, caching, RAG/embeddings, function calling) and user-centered iteration with a non-technical product stakeholder.”

“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”

“ML/AI engineer focused on production NLP and voice AI systems in the restaurant tech space, with hands-on work spanning ASR, intent classification, LLM fine-tuning, and deployment monitoring at Presto AI. They highlight a 15% improvement in full-AI ordering rate and also built a restaurant sentiment analysis product at Wisely that they say became a standout feature in a $10M acquisition context.”

Chaitanya Prasad Reddy Narala

Screened

Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems

USA4y exp

“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”

Sachin Komati

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG, and healthcare ML

Florida, USA5y exp

“Built an end-to-end GenAI/RAG platform for financial compliance and research at BlackRock, focused on safe, auditable answers in a highly regulated environment. Combines strong LLM engineering depth with production platform skills and delivered clear business impact, including reducing research/compliance turnaround from hours to seconds, improving retrieval relevance by 22%, and cutting inference costs by 75%.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”

“AI/ML engineer with production experience building LLM-powered document intelligence and customer support systems in healthcare/insurance, emphasizing high-accuracy RAG, long-document processing, and robust monitoring/fallback mechanisms. Also automates and scales ML lifecycle workflows using Apache Airflow and Kubeflow, and partners closely with non-technical operations stakeholders to drive adoption.”

“ML/NLP practitioner with experience building a transformer-failure prediction system that combines sensor signals with unstructured maintenance comments using LLM-based extraction and similarity validation. Strong emphasis on production readiness—data leakage controls, SQL-driven data quality tiers, and rigorous bias/fairness validation (including contract/spec evaluation across diverse company profiles).”

“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”

“Serial entrepreneur who built Verb Technology from a garage startup to a Nasdaq IPO, raising multiple rounds of capital along the way. Invented interactive live streaming technology that was acquired by Amazon and demonstrated rapid product/market response during COVID by prototyping and launching a solution for users while tightly managing AWS costs.”

“Data engineer with experience delivering an end-to-end pipeline handling ~3.5TB in a star-schema setup (fact + dimensions) and producing business-facing tables in Hive/Spark. Identified and resolved UAT-reported duplicate issues caused by joins through root-cause analysis, and also built automation to run Spark SQL metrics on weekly/monthly/quarterly cadences and distribute results to users.”

“Data engineer focused on fraud and banking analytics who has owned end-to-end batch + streaming pipelines at very large scale (hundreds of millions of records/day). Built robust data quality/observability layers (schema validation, anomaly detection, alerting) and delivered low-latency serving via AWS Lambda/API Gateway with DynamoDB + Redis, plus external data ingestion/scraping pipelines orchestrated in Airflow with anti-bot protections.”

Abhinav Gupta

Screened

Junior Machine Learning Engineer specializing in LLMs and applied data science

2y exp

“Built and shipped multiple production AI systems, including Auto DocGen (LLM-generated OpenAPI docs kept in sync via AST diffs, schema-constrained generation, and CI/CD on Render) and a multimodal sign-language recognition pipeline at USC orchestrated with FastAPI, MediaPipe, and PyTorch. Also partnered with Esri’s non-technical community team to fine-tune an LLaMA-based spam classifier with a review UI, cutting moderation time by 70%.”

Ajay Madhusudhan Thumala

Screened

Junior Software Engineer specializing in data engineering and LLM applications

Irvine, CA1y exp

“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”

Aditya Jhaveri

Screened

Mid-level Software Engineer specializing in AI, big data, and distributed systems

Jersey City, NJ3y exp

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

Sanjana Duvva

Screened

Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps

5y exp

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”